Annotate French Clinical Data Using Large Language Model Predictions
Simon Méoni, Théo Ryffel, Éric Villemonte de la Clergerie · 2023
In clinical and other specialized domains, data are scarce due to their confidential nature. This lack of data is a major problem when fine-tuning language models. Nevertheless, very large language models (LLMs) are promising for the medical domain but cannot be used directly in healthcare facilities due to data confidentiality issues. We explore an approach of annotating training data with LLMs to train smaller models more adapted to our problem. We show that this method yields promising results for information extraction tasks.